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International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 10, Issue 6 (June 2014), PP.50-58
50
Grid Stability Improvement in Deregulated Environment
Using HVDC and Fuzzy Controller
Dr. P. R. Sharma1,Anu Garg2 Chairman EE Department1, YMCA University of Science and Technology, Faridabad, India
PG Student Power system& 𝑑𝑟𝑖𝑣𝑒𝑠2, YMCA University of Science and Technology, Faridabad, India
Email: [email protected]
Email: [email protected]
Abstract- In this paper, two equal areas is considered. This paper deals with the results of dynamic performance
of frequency control and tie line. A fuzzy logic controller is proposed for two area power system interconnected
via parallel HVAC/HVDC transmission link which is also referred as asynchronous tie lines. The linear model
of HVAC/HVDC link is developed and the system responses to sudden load change are studied. For obtaining
least deviations we have formed a deregulated system of Wind Turbine Generator for both system and tie line
separately. Then these systems are applied to our original systems and tie lines. The deviations have decreased
appreciably.
Keywords:- Automatic generation control (AGC) Deregulated environment, Fuzzy logic controller, Generation
rate constraint, Area control error, HVDC Link
I. INTRODUCTION The Grid Stability is a technical requirement for the proper operation of an interconnected power
system and it is the prerequisite for a stable electricity grid and guarantees secure supply at a frequency of 50 Hz
[1]. As far as production is concerned, this is relatively simple: the production of electrical energy is largely
foreseeable, apart from the new renewable energies [2]. An interconnected power system consists of
interconnected control areas. A worldwide trend in the development of power systems is to build
interconnections with the goal to achieve economical benefits. Automatic Generation Control (AGC) is used to
maintain the schedule system frequency. With the advancement of systems analysis techniques such as optimal
control theory, their application to power system problems was inevitable. In particular, there are three
significant reasons to search for better control strategies for the AGC problem. First, the continuing growth of
this nation has been and will continue to be achieved at the expense of an enormous consumption of electrical
energy, thus causing a demand for more efficient use of generation facilities. Second, careful usage of
generating facilities reduces the ecological impact through reduction in fuel consumption and discharge
problems [4]. Finally, the trend is toward larger units, which increases the problems of system stability. Thus,
better control strategies are required in improving stability margins and overall power system reliability.
Controlling the frequency has always been a major subject in electrical power system operation and is becoming
much more significant recently with increasing size, changing structure and complexity in interconnected power
systems. Next importance is given to the use of High Voltage DC transmission (HVDC) link in the system
rather than High Voltage Alternating Current (HVAC) transmission only. HVDC is a foreseen technology due
to huge growth of this transmission system and due to its economic, environmental and performance advantages
over the other alternatives [1]. Therefore it is proposed to have a dc link in parallel with HVAC link
interconnecting control areas to get an improved system dynamic performance. These studies are carried out
considering the nominal system parameters. Intelligent controllers are designed using proportional-plus-integral
control strategy and implemented on the system under consideration in the wake of 1% step load perturbation in
the interconnected area. The conventional control method does not give required solutions due to complex and
multivariable power systems. Therefore next step is taken to improve the reliability and robustness of the system
using Fuzzy Controllers [10]. Fuzzy Controllers are advantageous in solving wide range of control problems
including AGC of interconnected power system. Fuzzy logic based controller can be implemented to analyze the
load frequency control of two area interconnected power system with HVAC and HVDC parallel link taking
parameter uncertainties into account. In the system working under deregulated environment, a Wind Turbine
Generator (WTG) or other locally generating plants can be simulated using in the to carry out all the proposed
operations and to control the frequency of the system using AGC and Integral Controller with the Fuzzy
Controller. The power system is modelled and simulated using MATLAB simulink environment. Then the
frequency deviation has been studied and presented with fuzzy controller, HVDC and WTG [3].
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
51
II. TWO AREA POWER SYSTEM The two area power system model identified in the present study has the following configuration;
(i) It is a two area interconnected power system consisting of identical single stage turbines.
(ii) The two areas are interconnected via HVAC tie line in parallel with HVDC link. The single line diagram of
the model under consideration for two areas is presented in Figure 1.The transmission links are considered as
long transmission lines specifically of length greater than break even distance length of HVAC and HVDC
transmission lines [5].
Figure1: Single line diagram of two area power system with parallel HVAC/HVDC links
III. FUZZY LOGIC CONTROLLER Because of inherent characteristics of the changing loads, complexity and multi-variable conditions of
the power system, conventional control methods may not give satisfactory solutions. Artificial intelligence
based gain scheduling is an alternative technique commonly used in designing controllers for non-linear
systems. Fuzzy system transforms a human knowledge into mathematical formula. Therefore, fuzzy set theory
based approach has emerged as a complement tool to mathematical approaches for solving power system
problems. Fuzzy set theory and fuzzy logic establish the rules of a nonlinear mapping [6]. Nowadays fuzzy logic
is used in almost all sectors of industry and science. One of them is automatic generation control. The main goal
of AGC in interconnected power systems is to protect the balance between production and consumption. The
fuzzy logic controller designed for the system analysis is shown in Figure 2.
Figure2: Components of fuzzy controller
The fuzzy logic controller is comprised of four main components: the fuzzification, the inference engine, the
rule base, and the defuzzification, as shown in Figure 4. The fuzzifier transforms the numeric/crisp value into
fuzzy sets; therefore this operation is called fuzzification. The main component of the fuzzy logic controller is
the inference engine, which performs all logic manipulations in a fuzzy logic controller. The rule base consists
of membership functions and control rules. Lastly, the results of the inference process is an output represented
by a fuzzy set, however, the output of the fuzzy logic controller should be a numeric/crisp value. Therefore,
fuzzy set is transformed into a numeric value by using the defuzzifier. This operation is called defuzzification.
For the proposed study, Mamdani [7] fuzzy inference engine is selected and the centroid method is used in
defuzzification process.
LN: large negative, MN: medium negative, SN: small negative, Z: zero, SP: small positive, MP: medium
positive and LP: large positive
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
52
Table 1.Rules for the fuzzy logic controller
A C' E
LN MN SN Z SP MP LP
A LN LP LP LP MP MP SP Z
C MN LP MP MP MP SP Z SN
E SN LP MP SP SP Z SN MN
Z MP MP SP Z SN MN MN
SP MP SP Z SN SN MN LN
MP SP Z SN MN MN MN LN
LP Z SN MN MN LN LN LN
Figure 3.Membership functions used in study
Fuzzy logic shows experience and preference through membership functions which have different shapes
depending on the experience of system experts [8]. These rules are obtained based on experiments of the process
step response, error signal, and its time derivative. The membership functions of the fuzzy logic pi controller
presented in Figure 3 consist of three memberships functions (two-inputs and one-output). Each membership
function has seven memberships comprising two trapezoidal and five triangular memberships. Seven numbers
of rules have been taken in inference mechanism. Therefore, 49 control rules are used for this study. The range
of X is selected from simulation results. All memberships are selected to describe the linguistic variables. These
functions have different shapes depending on system experts’ experience. For the determination of the control
rules, it can be more complicated than membership functions, which depend on the designer experiences and
actual physical system. The control rules are build from the if-then statement (i.e. if input 1 and input 2 then
output 1). Table 1 taken from [12], indicates the appropriate rule base used in the study. Let us consider the
fourth row and fifth column in Table 1 e.g. if ACE is Z and AĊE is SP then y is SN.
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
53
IV. WIND TURBINE GENERATOR AS DEREGULATED SYSTEM The dynamic modelling of wind power generators (WTG), in order to estimate their impact on the
power system dynamic behaviour, is a matter of high interest. The ‘Wind Power Model’ contains the following
basic formula to calculate the turbine mechanical power. The two transfer functions are used here for low pass
filter and for high wind speed. In the upper part the wind speed is low-pass filtered. This time constant depends
on the average wind speed, but is assumed constant for this simplified model. The lower part is used for
maintaining high speed of wind and then both are added as shown in figure4.
Figure 4.Wind turbine generator used as deregulated system
V. WTG AS LINEAR FUNCTION IN TIE LINE The output of wind turbine generator depends on the wind speed at that instant. The wind turbine
system contains several nonlinearities. When a wind turbine uses its pitch controller to counteract utility grid
frequency oscillations, its output power varies between maximum, or rated power, and zero power. In general, a
necessary condition for a linear system can be determined in terms of an excitation x(t) and a response y(t).
Suppose that the system at rest is subjected to an excitation x1(t) and the result is a response y1(t). Also suppose
that when subjected to an excitation y1(t) the result is a corresponding response y2(t). For a linear system it is
necessary that the excitation x1(t) + x2(t) results in a response y1(t)+ y2(t). This is usually called the principle of
superposition. Thus the wind turbine can be simplified to a first order system. The transfer function of the WTG
and generator is represented by a first-order lag [11] as
Gt(s) = 1/(1+sWt)
Gg(s) = 1/(1+sWg)
The transfer function of two area interconnected system using HVDC and fuzzy controller is shown in figure 5.
Figure5- Two area interconnected system using HVDC and Fuzzy controller
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
54
V. SIMULATION RESULTS In this paper, a fuzzy logic based proportional integral (pi) controller has been designed and applied to
analyze the effect of HVDC link on the AGC of two area interconnected power system. The implementation
worked with Matlab-Simulink software. The response plots for variables like frequency deviations in area 1 to
area 2 and tie line power deviations for power system model with and without HVDC link and wind turbine
system, in the wake of load disturbance of 1% in area 1 are obtained with the implementation of Fuzzy logic
controller to analyze the system dynamic performance. Figure 6 shows the frequency deviation responses and
Figure 7 shows some tie line power deviation responses .The simulation results of frequency deviations and tie
line power deviation with fuzzy controller advocates, the HVDC link’s suitability for AGC schemes.
(I) ΔF1(s) vs. time(s) of the power Subsystem1 model with Fuzzy Conroller only
(ii) ΔF2(s) vs. time(s) of the power Subsystem2 model with Fuzzy Controller only
(iii) ΔF1(s) vs. time(s) of the power Subsystem1 model with Fuzzy Controller and HVDC link
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
55
(iv) ΔF2(s) vs. time(s) of the power Subsystem2 model with Fuzzy Controller And HVDC
(v) ΔF1(s) vs. time(s) of the power Subsystem1 model with Fuzzy Controller, HVDC and WTG
Figure6- Frequency Comparison
(vi) ΔF1(s) vs. time(s) of the power Subsystem2 model with Fuzzy Controller, HVDC and WTG
(i) Tie Line Power Deviation of area 1 With Fuzzy Controller only
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
56
(ii) Tie Line Power Deviation of area 2 With Fuzzy Controller only
(iii) Tie Line Power Deviation of area1 With Fuzzy Controller and HVDC
(iv) Tie Line Power Deviation of Area2 with Fuzzy Controller and HVDC
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
57
(v) Tie Line Power Deviation of area 1 with Fuzzy Controller, HVDC and WTG
(vi) Tie Line Power Deviation of area2 with Fuzzy Controller, HVDC and WTG
Figure7. Tie Line deviation
VI. CONCLUSION In this paper, a new power system model is proposed to improve the dynamic performance of
interconnected two area power system by the use of HVDC link in parallel with existing HVAC link. The
system dynamic performance in the wake of load disturbance in either area of interconnected power system has
been investigated comprehensively. A fuzzy logic control strategy is designed and its feasibility is studied by
varying system parameters. It has been observed that responses of the system with parallel HVDC link and
WTG are better in terms of dynamic parameters such as peak overshoot and settling time. Simulation results
presented justify the incorporation of HVDC transmission link to supply consumers reliable and quality power.
APPENDIX Nominal parameters of the two area system investigated:
Ki1=Ki2=0.09
Tg1=0.08
Tg2=0.04
Tt1=0.5
Tt2=0.3
Tdc1=Tdc2=0.2
Kps1=100
Kps2=120
Wt=1.5
Wg=0.5
Num(s) =0.05
B1=B2=0.42
K=0.33
Grid Stability Improvement In Deregulated Environment Using HVDC And Fuzzy Controller
58
REFERENCES [1] Yogendra Arya, Narender Kumar, Hitesh Dutt Mathur, “Automatic Generation Control In Multi Area
Interconnected Power System by using HVDC Links” IJEPDS(International Journal of Power
Electronics and Drive System)Vol.2,No.1,March2012, pp.67~75, ISSN :2088-8694.
[2] The Grid of Tommorow, http://www.swissgrid.ch/swissgrid/en/home/grid/development.html
[3] I.J. Nagrath and D.P. Kothari, “Modern Power System Analysis” 3rd Edition, Sixteenth reprint 2009,
Tata Mc-Graw Hill publication.
[4] Naimul Hasan, “An Overview of AGC Strategies in Power System” IJETAE (International Journal of
Emerging Technology and advance Engineering), Vol.2, Issue 8, August 2012 ISSN 2250-2459,pp
56~64.
[5] Ibraheem and P. Kumar, “Study of dynamic performance of power systems with Asynchronous tie line
considering parameter uncertainities,” Journal of Institution of Engineers (I)’ vol.85, pp. 35-42’2004.
[6] Z.A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, issue 3, pp. 338-353, June 1965.
[7] S.G. Cao, N.W. Rees and G. Feng, “Mamdani-type fuzzy controllers are universal fuzzy controllers,”
Fuzzy sets and systems, vol. 123, issue 3, pp. 359-367, 2001
[8] K.Tomsovic,“ChapterIV-Fuzzy systems applications to power systems,” in Proc. of International
conference on intelligent system application to power systems, Brazil: Riode Janeiro, 1999, pp. 1-10.
[9] Manuel A. Matos, Eduardo M. Gouveia, “The Fuzzy Power Flow Revisited” IEEE Transactions
onPower Systems, Vol. 23, No. 1, February 2008,pp213~218.
[10] Mrinal K. Pal, “Lecture Notes on Power System Stability” Online Stability book, June 2007.
http://www.scribd.com/doc/39610235/Stability-Book//.
[11] I.Kocaarslan and E.Cam, “Fuzzy logic controller in interconnected electrical power system for load
frequency control,”Electrical power and energy systems, vol.27, issue 8 pp.542-549,2005.